AI Threat Modeling
AI threat modeling you can actually audit.
A generic LLM can list threats in seconds. Everything after that — grounding, traceability, and defensibility — is where thin wrappers fall apart. Threatlas runs AI over a structured security knowledge graph, so every output is reproducible and every mapping is auditable.
What is AI threat modeling?
AI threat modeling uses large language models to identify threats, propose countermeasures, and keep a system's threat model current as it changes. The hard part isn't generating a list — it's making that output trustworthy: grounded in real security knowledge, traceable to evidence, and consistent enough to stand up in an audit. That's the difference between a security platform and a chatbot with a prompt.
A harness for AI — not a wrapper around it
At the core is a structured security knowledge graph. Multi-LLM agents reason over it, so their answers are anchored to a real security data model — not invented on the spot.
Threats
A curated library of thousands of threats, classified by STRIDE and linked to MITRE ATT&CK and CWE.
Controls
Countermeasures mapped to frameworks via an OpenCRE-based spine, so a single control satisfies many standards.
Frameworks
OWASP ASVS, NIST 800-53, ISO 27001, PCI DSS, CIS — plus any custom standard you define.
Components
A model of your architecture — zones, services, data stores, trust boundaries — that the AI reasons over.
All linked, all queryable — so threats, controls, and frameworks reinforce each other instead of drifting apart.
Where the AI does the work
Read your architecture
AI extracts components, data flows, and trust boundaries from source code, documents, and diagrams — turning prose and code into a structured model.
Propose threats & countermeasures
Reasoning over the knowledge graph, the model surfaces relevant threats and the controls that mitigate them — grounded in a security data model, not free association.
Turn policies into controls
AI reads your security policies and standards documents and turns them into structured, mapped controls — so your own requirements become part of the model instead of a PDF nobody opens.
Answer in natural language
Ask questions and edit your threat model in plain language — the AI translates intent into precise operations on the underlying model.
Your models. Your data boundary.
Threatlas runs on leading frontier models — and supports bring-your-own-model: self-hosted open models such as Qwen and Llama can run fully on-premises for air-gapped, regulated environments. It's paired with data-residency options and zero data retention, so your code and architecture never leave the boundary you choose.
That's what lets regulated teams adopt AI threat modeling without handing their source to a third-party model they don't control.
AI threat modeling FAQ
What is AI threat modeling?
AI threat modeling uses large language models to help identify threats, propose countermeasures, and keep a system’s threat model current. The quality depends entirely on the design: a model grounded in a structured security knowledge base produces reproducible, defensible results, while a thin LLM wrapper tends to produce generic, unverifiable output.
How does Threatlas avoid AI hallucination?
Threatlas is a harness for AI, not a wrapper around it. Multi-LLM agents reason over a structured security knowledge graph — threats, controls, frameworks, and your components, all linked — so outputs are grounded in a real security data model. Every threat and mapping is traceable to its source, and a human reviews before anything is accepted.
Which AI models does Threatlas use, and can we run our own?
Threatlas runs on leading frontier models and supports bring-your-own-model (BYOM): self-hosted open models such as Qwen and Llama can run fully on-premises for air-gapped, regulated environments. It pairs with zero data retention and data-residency options so inputs and outputs stay under your control.
Is AI-generated threat modeling defensible in an audit?
Yes — that is the design goal. Because every output is grounded in the knowledge graph and traceable to its source, Threatlas produces audit-ready artifacts: mapped requirements and traceable documentation, with a human in the loop on every decision.
See grounded AI threat modeling in action.
Threatlas is in private beta with a select group of security teams. Request a demo to see how grounded, auditable AI threat modeling works on your stack.
Request a demo